OutcomeObj-class

Class <code>OutcomeObj</code> groups outcome regression results under a common
 name

internal

Methods to estimate dynamic treatment regimes using Interactive
Q-Learning, Q-Learning, weighted learning, and value-search methods based on
Augmented Inverse Probability Weighted Estimators and Inverse Probability
Weighted Estimators. Dynamic Treatment Regimes: Statistical Methods for
Precision Medicine, Tsiatis, A. A., Davidian, M. D., Holloway, S. T., and Laber, E. B.,
Chapman & Hall/CRC Press, 2020, ISBN:978-1-4987-6977-8.

Shannon Holloway

DynTxRegime

Methods for Estimating Optimal Dynamic Treatment Regimes

OutcomeObj-class function

<dl>
<dt><code>outcome</code></dt>
<dd>ANY - expected to be <code>OutcomeNoFit</code>, 
<code>OutcomeSimpleFit</code>,
<code>OutcomeSimpleFit_fSet</code>, <code>OutcomeSimpleFit_SubsetList</code>, 
<code>OutcomeIterateFit</code>, or <code>DecisionPointList</code>.</dd></dl>

Slots

Class OutcomeObj — OutcomeObj-class

<dl>
<dt><code>outcome</code></dt>
<dd>ANY - expected to be <code>OutcomeNoFit</code>, 
<code>OutcomeSimpleFit</code>,
<code>OutcomeSimpleFit_fSet</code>, <code>OutcomeSimpleFit_SubsetList</code>, 
<code>OutcomeIterateFit</code>, or <code>DecisionPointList</code>.</dd>


</dl>

OutcomeObj-class: Class `OutcomeObj`

Description

Arguments

Slots